Scholarlink

<p>Floriculture is a vital agricultural sector that significantly contributes to the local and regional economies. Proper identification and mapping of floricultural resources are key to effective planning and crop management, especially in communities whose livelihoods rely on this industry, promoting sustainable growth and stability. This study focused on mapping various flower and crop types in the Debra and Panskura blocks of West Bengal (India) using remote sensing based satellite products and machine learning techniques. Accurate mapping of crop types using conventional satellite datasets remains constrained by inherent limitations, including persistent cloud cover, coarse spatial resolution and related factors. To address this issue, this study utilizes high-resolution multispectral (Sentinel-2) and microwave (Sentinel-1) data collected between November 2022 and April 2023 to improve crop and flower type identification. Various combinations of Sentinel-1 and Sentinel-2 datasets, along with spectral indices Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI) were analyzed using two machine learning algorithms—Random Forest (RF) and Support Vector Machine (SVM). Classification performance was assessed based on overall accuracy, kappa coefficient, and standard deviation. Results showed that integrating multiple datasets significantly enhanced accuracy, exceeding 80%, compared to using individual datasets. Among the models evaluated, RF demonstrated superior performance over SVM in accurately identifying different flower and crops. The findings demonstrate that combining remote sensing data with machine learning can reliably distinguish different crops and flower types, providing valuable insights for agricultural management, ecological modeling, and large-scale crop mapping efforts.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Identification of floriculture using multi-temporal Sentinel datasets and machine learning techniques

  • 期刊论文

  • 发表日期：

    2025年10月30日

查看全文
  • Shrabani Kar,
  • Debarati Bera,
  • Dipanwita Dutta,
  • Arnab Kundu

摘要

Floriculture is a vital agricultural sector that significantly contributes to the local and regional economies. Proper identification and mapping of floricultural resources are key to effective planning and crop management, especially in communities whose livelihoods rely on this industry, promoting sustainable growth and stability. This study focused on mapping various flower and crop types in the Debra and Panskura blocks of West Bengal (India) using remote sensing based satellite products and machine learning techniques. Accurate mapping of crop types using conventional satellite datasets remains constrained by inherent limitations, including persistent cloud cover, coarse spatial resolution and related factors. To address this issue, this study utilizes high-resolution multispectral (Sentinel-2) and microwave (Sentinel-1) data collected between November 2022 and April 2023 to improve crop and flower type identification. Various combinations of Sentinel-1 and Sentinel-2 datasets, along with spectral indices Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI) were analyzed using two machine learning algorithms—Random Forest (RF) and Support Vector Machine (SVM). Classification performance was assessed based on overall accuracy, kappa coefficient, and standard deviation. Results showed that integrating multiple datasets significantly enhanced accuracy, exceeding 80%, compared to using individual datasets. Among the models evaluated, RF demonstrated superior performance over SVM in accurately identifying different flower and crops. The findings demonstrate that combining remote sensing data with machine learning can reliably distinguish different crops and flower types, providing valuable insights for agricultural management, ecological modeling, and large-scale crop mapping efforts.